Data Processing Advanced (DPA) (880082-M-3) - Summary English
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Course
Data Processing Advanced (880082M3)
Institution
Tilburg University (UVT)
Data Processing Advanced (DPA) (880082-M-3) - Summary English. Quick and very readable overview of the needed theory for the questions on the exam. Very easy to print and to use during your exam preparations.
CREATING ARRAYS
np.arange()
np.arange(1, 11.9, 2.1) à Starting point (1), End point (11.9) (Excluded) and the Steps taken (2.1)
np.linspace() à Array with an starting point, end point and amount of numbers. EQUALLY SPACED
np.linspace(0, 2, 9) à Starting point (0), End point (2) (Included) and the amount of numbers we
want (9)
np.zeros([])
np.zeros()
np.zeros(5) à Array with 5 zeros
np.ones() à Array with ones
np.random.random()
np.random.random(4) à 4 Random numbers between 0 and 1
np.random.randint()
np.random.randint(5, 10, 12) à Starting point (5), End point (10) (Excluded) and Number of Intergers
(12)
np.random.unifrom()
np.random.uniform(5, 10, 12) à Array with 12 random numbers with uniform distribution between 5 and
10
np.random.normal()
np.random.normal(5, 3, 10) à Array with 10 random numbers with normal distribution with mean 5 and
SD of 3
a = np.random.uniform(1, 100, 100000)
np.size() à Size of array in terms of numbers
np.size(a) = 100000
np.ndim() à Dimension of array
np.ndim(a) = 1
np.shape() à Shape of the array
np.shape(a) = (100000,)
CHECKING EQUIVALENCE
a = np.array([2, 4, 6, 8])
a = b
np.allclose() à Checks if two arrays are the same
np.allclose(a,b) = True
CHECK LOCATIONS OF TWO ARRAYS IN COMPUTER MEMORY
id() à Checks locations of arrays
id(a) == id(b) = True
, DATA TYPES IN ARRAYS
dtype() à Datatype of the array
The 6 basic data types of Numpy arrays are:
- float (float16, float32, or float64)
- integer (int8, int16, int32, or int64)
- unsigned integer: this number cannot be negative (uint8, uint16, uint32, or uint64)
- boolean (bool)
- complex (complex64 or complex128)
- string (for example <U3 or <U64, where the number indicates the maximum length of the strings)
X = np.array([1, 3, 5, 7])
x.dtype = int8
itemsize() à Checks how many items there are in the array
x.itemsize = 4
dtype=’ ‘ à Changing data type in the array
dtype=’float’ = [1. 3. 5. 7.]
INDEXING IN ARRAYS
a = np.arange(10)
a = [0 1 2 3 4 5 6 7 8 9]
print( b[0] ) #First element of the list = 1
print( b[3] ) #Fourth element = 4
print( b[9] ) #Tenth element = 10
print( b[-1] ) #Last element. First of reversed order. = 10
print (b[1]) = 2
print( b[:] ) #All elements, from first to last = [1 2 3 4 5 6 7 8 9 10]
print( b[3:6] ) #Thrird element to fifth element (Excluded) = [4 5 6]
print( b[:4] ) #First to third = [1 2 3 4]
print( b[-4:] ) #Last four = [7 8 9 10]
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